14 FEBRUARY 2017
How Much Mathematics Can You Eat?
PROFESSOR CHRISTOPHER BUDD
Introduction
A universal constant amongst all animals is the need to eat. The appreciation of food and drink has been one of
the greatest forces moulding our lives both from a point of view of day to day existence, and also our sense of
taste and aesthetics. Food truly brings us all together, and without it we would all surely die. The food and drink
industry is the largest in the world and in order to feed the growing population of the world we will have to
grow more food in the next 50 years than we have in the last 10,000 years.
In this talk we will look at the role that mathematics plays in the production, cooking and consumption of food
and drink, taking you from ‘farm to fork’ with a series of case studies based on my own experiences. I hope that
you will all enjoy this rich diet of mathematics.
Some Basic Facts
What does maths do to help the starving people in Africa? This is a question I am not infrequently asked when
I give talks to schools, where the pupils are not aware of the many applications of maths to the real world. The
simple answer to this question is that maths helps a very great deal, by ensuring that they will be fed. Let us
think about the various processes involved. In the case of arable farming this involves planting the seeds,
watering them and letting them be pollinated and then grow. It may be also necessary to apply pesticides at
some point, and to understand the weather well enough to know when to harvest. Following harvesting the
food must be transported to where it is needed. Other types of food, such as cattle, pigs or chicken, must be
raised carefully and allowed to grow. Chicken eggs need to be incubated and foodstuffs for the animals need to
be delivered on time. Once the animals have been slaughtered for their meat, it is usually refrigerated and stored.
This has to be done very carefully to make sure that it is safe, and that the meat will be fresh when it is thawed
out. This meat also needs to be delivered to the customer in such a manner that it does not lose its freshness.
Once at the consumer the food must be cooked safely, eaten and finally digested. Drinks must also be prepared
carefully. Mathematics plays a vital role in ensuring that we have safe water to start with. It helps brewers in
controlling the fermentation and production process, and in storing beer to avoid sedimentation. It also, as we
shall see, helps to put the bubbles in beer and stout.
Sometimes when I talk about food, I am told off for using maths and science on ‘something so trivial’. The
government disagrees. As a measure of its importance, agricultural science is listed as one of HM Government’s
Eight Great Technologies in a list published in 2012 (see more details about this list in my first two talks). The UK
agri-food industry alone contributes around £100Bn annually to the economy. As part of this the drink industry
contributes £18Bn, with 5 Billion pints of beer drunk per year (which works out as 2 pints per week per head of
the population). Food related companies employ mathematicians, sometimes in large numbers. In my second
Gresham lecture on Big Data, I explained the role that statisticians play in the retail industry, but mathematicians
are also in demand well before the food reaches the shop shelves. As an example, mathematicians work at the
heart of the chocolate industry. It is hard (for me) to think of a better occupation than being a chocolate
mathematician.
It all starts in a field
Apart from fish (which we will come back to later), most of our food production, whether it is crops or animals,
involves a field on a farm (or its close relative and orchard or a vineyard). This leads to the often quoted phrase
‘food from farm to fork’. Whilst it might look on first inspection to be a low technology item, a lot of science and
mathematics is involved in making a field effective for food production. Indeed there are sophisticated
computer packages which are used to simulate the behaviour of a field and to advise farmers on the best way to
manage the fields on their farm. The basic questions that need to be addressed by a farmer growing crops are:
what crops to grow and how much, how much to irrigate them, what pesticides should be used, how to react to
the weather and when to harvest. The first of these questions involves the mathematics of optimisation. It is
perhaps useful to give an idea of how this might work with a simple field (or maybe several fields) on a farm.
Let’s consider an actual example of a farm somewhere in the tropics in which we want to grow two crops, such
as cocoa and pineapples. If c is the amount of cocoa we plan to grow in one year, and p is the amount of pine
apples, and the unit cost of cocoa seed is a, and of pineapple seed is b, then the total cost C of growing the two
crops is given by
C = a c + b p + d.
(1)
Where d is the upfront cost we must take on just to use the field in the first place (such as labour, irrigation,
pesticides etc.). Similarly, when we harvest the crops we might expect a unit return of e on the cocoa, and of p
on the pineapples. Thus we might make a profit P given by
P = e c + f p.
(2)
Finally, if the amount of space taken up by a unit cocoa is g and by a unit pineapple is h then the total amount
of space S taken up in the field by our two crops is given by
S = g c + h p.
(3)
The problem faced by our farmer is then as follows. They want to grow the right amount of cocoa c and
pineapples p which in turn maximises their profit P. But at the same time they must also want to keep the cost C
below some maximum C_max, (their total available cash) and require that the space taken up is less than the
total area of the field given by S_max. These two conditions are called constraints. In mathematical terms the
problem of maximising the profit becomes
Maximise
P
over all positive values of c and p.
subject to
C < C_max
and
S < S_max.
(4)
Problem (4) is a special example of a constrained optimisation problem called a linear programming
problem. In the case of our problem above this can be solved by using a graph and we show an example in
which the optimal combination of c and p is highlighted. It occurs at the corner of the shaded four sided figure
bounded by the axes and the lines defined by the two constraints.
Constraints
p
2
Op mal solu on
c
More generally of course, a farmer will have many more crops, or even animals, to consider, as well as many
more constraints, such as labour costs, irrigation costs, etc. as well as considering the resistance of each crop to
disease. This leads to more complex problems similar in form to (4) involving many more variables and
constraints. Not dissimilar problems arise in many other applications, such as in retail and in transport systems.
A key feature of problem (4) is that it is linear which means that we see c and p and their multiples in it, but not
more complicated functions such as c^2, c^3 or c*p. We call such a problem a linear constrained optimisation
problem. Remarkably, despite their apparent complexity, there is an algorithm to solve all such linear problems.
It is called the Simplex Algorithm, and its invention in 1947 by Dantzig was one of the key developments in
mathematical algorithms in the 20th Century. Today countless optimisation problems are solved by the Simplex
Algorithm, ranging from farming to some of the most complex problems in economics and scheduling. More
information about the Simplex Algorithm can be found in [1] and [2].
Things get a bit more complicated when we have to consider the effects of weather and climate. In a future talk
I will talk in some detail about the mathematics behind this complicated subject, but it is obvious (and has been
for eternity) that the weather has a huge effect on the productivity of a farm. As an example we can look again at
the production of cocoa. In the following two figures we show the total yield of cocoa in Ghana each year and
compare it with the mean annual temperature and also the maximum mean monthly temperature in the same
year. As you can see there is a close correspondence between the two. More information can be found in [3]
Farmers must thus plant their crops, taking into account the predicted effects of the weather. In the past this
was largely a matter of luck combined with experience. However now it is possible to gain reasonable estimates
of what weather to expect by using forecasts based on a combination of statistics and mathematical modelling.
Being able to model the potential variation in yields as a result of understanding the weather better enables
companies that work with the famers and buy their crops to offer targeted advice to small holder farmers on
how to improve their yields, and help secure the development of sustainable farming communities. This work
has the potential to make a real difference to the lives of farmers in third world countries
Mathematicians have also considered the growth of crops for the whole of the world. This is important if we are
to grow enough to feed the whole of the world’s population. The problem of how things grow, was studied in
the classic text by D’Arcy Thompson [4] (now the subject of a major art exhibition in Edinburgh.) If a relatively
small amount of a crop is planted and allowed to grow year on year (during which it will be pollinated), then the
rate of growth of the crop is proportional to the amount of the crop. We can express this as a differential equation
of the form
dc/dt = a(t) c,
(5)
where the constant of proportionality a(t) will depend upon factors such as the weather and the effects of
irrigation and of pests. This equation works well if c is small, but as it gets larger, so more resource is needed to
grow the crops, and the rate of growth of the crop slows down. Also, when c is large enough we will want to
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harvest it at a rate proportional to the amount of the crop. These effects are well captured by the so-called logistic
equation introduced by Verhulst in 1838:
dc/dt = a(t) c ( k – c ) - b(t) c,
(6)
where k is an upper bound for the amount of crops and b(t) is the harvesting rate. This equation can be solved
to find the amount c(t) of the crop, and can give a very useful prediction of its value if different harvesting
strategies are employed.
An equation similar to (6) was originally devised by Malthus to study population growth in human populations.
Such is the power of applied mathematics, that the logistic equation can be used in many other areas related to
the supply of food. One of these is fishing, where now c(t) gives the numbers of a fish species, and b(t) a
strategy for catching the fish. Extra terms need to be added to allow for the movement of fish into and out of
the fishing area, but the basic equation remains the same. See [5] for more details and other extensions to the
model. Models for the populations of fish allow managements of fisheries to determine what level of fishing is
possible to ensure that there is a sustainable fish population.
I should also point out, that whilst I have considered the role of maths in food production from fields or from
the sea, there are other means of producing food which will become increasingly important as our need for food
gets greater. One of these is hydroponics in which food is grown on large water bags. Essential to the success of
this process are the sciences of fluid mechanics and structural mechanics. Mathematics plays a key role in both.
Mathematics is also hugely important in the correct application of fertilisers and insecticides and many other
processes.
How to keep food fresh and test it for freshness
Once food has been produced it needs to be delivered to where it can be eaten. Usually it is not possible to eat it
at once, and it must be stored in such a way that it remains fresh. An early method of doing this was to salt it.
However, a major breakthrough came with the widespread introduction of food refrigeration in the 19 th and 20th
Centuries. Now we of course take the refrigeration and freezing of food for granted. However, the revolution
in cooling and freezing food was due in no small part to the discovery, and mathematical formulation, of the
laws of thermodynamics by Kelvin and others in the 19th Century. These allowed the development of efficient
refrigeration devices based upon the expansion of gases. When freezing food it is important to cool it at the
correct rate to ensure that it is uniformly frozen.
(7)
𝐸𝑡 = 𝜅𝑇𝑥𝑥
Where T is the temperature of the food, and E is its Enthalpy which is a combination of the energy used to heat
the food plus the latent heat used to convert ice into water. When the food freezes it forms a freeing front at a
location s(t), the speed of which can be found by solving a Stefan Problem.
𝑑𝑠
𝑢𝑡 = 𝑢𝑥𝑥 , 𝑢(𝑠(𝑡), 𝑡) = 0,
= −𝑢𝑥 (𝑠, 𝑡)
(8)
𝑑𝑡
By solving this equation it is possible to plan and control the freezing process for a wide range of different foodstuffs. Once food is frozen it is stored in refrigerated cabinets, rooms, trucks, buildings and warehouse, some of
which can be as large as a football pitch. Such storage can bring its own problems. For example, what happens if
the door of the warehouse is left open for too long. By calculating the transfer of the heat within the warehouse
using the equation (7), it is possible to provide careful guidance for the safe time that this can happen without
the frozen food deteriorating to a point where it is not safe to eat. Such procedures can potentially save huge
amounts of food going to waste, and allow it to be stored and transported safely.
One of the more interesting problems that I have had to work on was that of finding out how fresh a fish is.
When fish are caught at sea they must be brought back to harbour and then sent on to (for example)
supermarkets. Clearly such retailers need to know how fresh the fish is. We often think that we can test the
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freshness of a foodstuff by its smell, but often food only starts to smell when it is far
from being fresh. So, with fish, a method for testing it had to be used which did not
(only) rely on its smell. The method we came up with was to look at the elasticity of the
skin, and the viscosity of the flesh beneath the skin. Both are closely related to the
freshness. For example our own skin becomes less elastic as we grow older. In order to
test this method we produced a mechanical probe to test the elasticity of the fish skin.
This probe bounced a small needle off the skin, and then monitored its response. By formulating a mathematical
equation for the expected motion of the skin, and comparing this with the measurements of the probe, it was
found possible to deduce successfully both the elasticity and the viscosity of the flesh, and hence the freshness
of the fish. I will talk more about these equations in some detail in my next talk about the mathematics of
materials.
Why a bunch of statisticians couldn’t organise a piss up in a brewery
In the year 2005 the British Science Festival came to Dublin. At that time I had the
honour of being the president of the maths section of the British Science Association,
which was organising the festival, and had the responsibility of devising a
mathematics programme for the event. One of our plans was to have a mathematics
visit to the Guinness Breweries in Dublin. Obviously there are many reasons why we
might want to visit a brewery, but why should mathematicians want to go there, and
why should they want to go to Guinness? The answer to both of these questions lies
in the person of Ghosset (pictured) who was the chief statistician at Guinness in the
first part of the 20th Century. Guinness was in many ways ahead of its time in the
production and quality control that it applied to its product (as well as the way that it
was advertised). Ghosset was employed in part to ensure that the Guinness stout was
of a consistent quality. This was done by making careful measurements of a sample of the product and using
these to assess both its general quality and its variability. This was, at the time, a difficult problem in statistics.
To solve it Ghosset devised a new statistical test to compare the measurements. This worked extremely well and
made a very real difference to improving the quality of Guinness stout. Ghosset felt it important to publish this
test, but was reluctant to disclose his identity and that of his employer. Instead it was published in the journal
Biometrika, in 1908, under the anonymous name of ‘Student’. Ever since this test has been known as Student’s ttest and it plays a central role in testing and maintaining the quality of food and drink all over the world.
So, let’s get back to the British Science Festival. Having decided to go to Guinness we set up a sub-committee to
organise the trip to it during the science festival, in part to celebrate the invention there of the t-test and its
contribution to modern statistics. Clearly such a trip should include a reception and a drink of a pint of
Guinness, Unfortunately, through no one’s fault, it wasn’t possible in the end to do this. It was only after the
event that we realised that we could then be accused of being unable to organise a piss up in a brewery.
I’m forever blowing bubbles
We have mentioned Guinness in the previous section. One of the key features of a pint of
Guinness is the wonderful creamy foam head. This is in contrast to the much smaller head
that we find on a pint of bitter beer. For the manufacturers of beer to get both types of
head involve a lot of science and maths. The foam in the head in a pint of bitter is made
of networks of bubbles of Carbon Dioxide separated by thin films of the beer itself, with
surface tension giving the strength to the thin walls surrounding each bubble. The walls of
these bubbles move as a result of surface tension with smaller bubbles moving faster as
they have a higher curvature. This results in the smaller bubbles being ‘eaten’ by the larger
ones in a process called Oswald ripening. Basically small bubbles shrink and large bubbles
grow, leading to a coarse foam made up of large bubbles only. Eventually the liquid drains from the large
bubbles and they pop, and the foam disappears. The remarkable mathematician John von Neumann, who was
(amongst many other achievements) responsible for the development of the modern electronic computer,
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devised an equation in 1952 which explained the patterns that we see in such cellular structures in two
dimensions. In 2007 this was extended to three dimensions by a group of mathematicians in Princeton
interested in the applications of maths the beer [6]. It’s a hard life!
Another group of mathematicians, appropriately from Limerick in Ireland, have made a
study of the foam on a pint of Guinness. This is much creamier than the foam on a pint
of bitter. The reason is that whilst the foam on bitter is made up of air bubbles, the
foam on a pint of Guinness is made up of Nitrogen. This gas diffuses 100 times slower
in air than Carbon Dioxide, meaning that the bubbles are smaller and the foam is much
more stable and creamier. The Nitrogen needs to be introduced into the Guinness
when it is poured. In a pub this is achieved by having a separate pipe, linked to a
Nitrogen supply, which supplied the Nitrogen at the same time as the beer is served
from the barrel. For many years Guinness in cans did not have a head. However, this problem was solved by the
introduction of a ‘widget’ which is a Nitrogen container in the can, and which releases precisely the right amount
of Nitrogen when the can is opened. This process must be very carefully controlled, and a lot of careful design
work is required to make the widget work well. The whole process was analysed by (it appears!) the whole of the
applied mathematics department at Limerick, and described in the charmingly title paper on The initiation of
Guinness [7]. Notably the same group has now done a complete analysis of the mathematics of a making coffee.
Which came first, the chicken or the egg?
The answer is of course the egg! Think about it. Maths isn’t needed to solve that question. However, it is
important both in the question of hatching an egg and also in helping the chickens to lay the eggs in the first
place. For chickens to able to lay healthy eggs, they must be healthy too. Keeping them healthy is a matter of
giving them a good diet and also a safe environment. Statistical techniques are used extensively to determine
good and healthy diets for all animals, and also to monitor how they
respond to their environment, so that it is never too hot or too cold.
(These tests are not unlike the clinical trials used by pharmaceutical
companies to test drugs before they are released.) Now consider what
happens when an egg is laid. If we want to breed more chickens these
eggs need to be incubated, and it is most efficient (in the case of large
numbers) to use an incubator to do this. Such incubators have to
carefully regulate their heat and humidity. Sophisticated incubators
also rotate the eggs during the incubation process. A problem that I
worked on, not with a chicken farm, but with the penguin house at Bristol Zoo, was that of determining the
optimal incubation process, and in particular the need to rotate the eggs laid by a penguin. Unfortunately, the
reason that we were asked to help was the fact that the zoo was finding that too many of the eggs in the
incubator were dying. During a natural incubation process a (male or female penguin as both are involved) sits
on the egg to keep it warm, and rotates it at the same time. In the case of natural incubation the egg is rotated
about once every 20 minutes. One theory behind the need for such a rotation was that it was needed to ensure
that the heat (from the penguin) was uniformly distributed. One of the purposes of mathematical modelling is to
test such theories to see if they are realistic. In this case it is possible to adapt the equation (7) to allow us to
predict the flow of heat within the penguin egg. The pictures below show the temperature of the egg at a set of
increasing times from left to right. In these pictures the penguin is at the top, and the brown areas are the
hottest, and the blue areas the coolest.
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As you can see the heat from the penguin spreads through the egg and eventually warms it all up. For the
particular case of a penguin egg we could calculate that this took about 10 minutes. In other words, there was
not need to rotate the egg at all to give it a uniform temperature. Thus the mathematical model was useful in
dismissing an incorrect theory. Further studies showed that the egg needed to be rotated to redistribute the
nutrients and to get rid of the waste products. Guided by this a better rotation strategy could be devised, leading
to an increase in the number of eggs that hatched into live penguins. More information about not only the
penguin egg problem, but also the process of mathematical modelling can be found in the book [9].
How to cook a potato
Before we eat food it is usual to cook it. This is one huge difference between
humans and other animals. In fact evidence for cooking food goes back a long
way, including evidence that our evolutionary predecessors Homo Erectus were
cooking food over 400,000 years ago. Of course cooking then was done over an
open fire. Nowadays it is more likely that we will use some kind of cooker. Until
recently, most cookers were based on heating the food directly, such as in a fan
oven, which cooks the food from the outside by conduction of heat into the food.
This process cooks the food uniformly, but can be slow. For example it takes about an hour to bake a potato in
a typical fan oven. Many of us now seek a more convenient and faster means of cooking, and this has led to the
rise of the use of the microwave cooker. The Microwave Cooker uses a technology which goes back to the war
and the invention of radar. In order to get a high resolution (particularly for airborne radar), radar systems
needed to use radio waves, called Microwaves, with a wavelength of the order of just a few centimetres.
However, in 1940 there was no means of producing radio signals at this wavelength in sufficient power to be
effective. Fortunately the University of Birmingham came to the rescue in the shape of the physics department
led by Prof. Oliphant. Working in this department were Randall and Boot who invented the first high power
cavity magnetron (pictured). This device used a high magnetic field to spin an electron beam and to cause it to
resonate in a specially designed cavity. The result was a method of producing high power radio waves of
kilowatt power and at Centimetre wavelengths. The magnetron revolutionised radar and was subsequently used
in all airborne interception radars and also in the H2S air to surface radar used in Lancaster bombers, as well as
in the radars used against U-Boats. It was truly a war winning invention (and was part of a package of secrets
taken to the USA by the scientific Civil Servant Sir Henry Tizard as part of the process of persuading them to
join in the war).
The Americans rapidly developed the technology for producing magnetrons in
large numbers. One of the scientists who did this was Percy Spencer, and he is
credited with the invention of the Microwave cooker. Legend has it that he did
this after noticing that his candy melted when exposed to high power microwave
radiation, and realised that the same radiation could be used to rapidly cook food.
Now microwave ovens, all powered by magnetrons, are very widely used in a
domestic environment. We are all used to opening up a microwave cooker, putting
in some food, pressing the button, and ping, five minutes later the food is cooked. Usually these ovens have a
turntable, and you can occupy yourself in those five minutes by watching the food go round. Mathematics is
very useful not only in understanding this process, but in helping to devise better and safer microwave cookers.
A commonly held view is that a microwave cooker cooks food from the inside out. However, this is not really
true. Microwaves from the magnetron enter the cavity surrounding the food, where they set up standing wave
patterns. These give points with a strong microwave fields alternating with points with a low field. To show
these points you are warmly recommended to take out the turntable, place several marshmallows in the cavity,
and then turn on the oven for a short time. You will find that some of the marshmallows have melted and
others have not. The melted ones are at the locations of the anti-nodes of the field, where it is strongest. As an
added bonus you can find the half wavelength of the field by measuring the distance between the melted
marshmallows. The reason that the food is placed onto a turntable is to make sure that no part of it is always at
a node of the field where it is at a minimum. During the cooking process, the microwaves from the cavity enter
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the food. As they do so, they rapidly lose strength, so that if E0 is the field on the surface, then at a distance x
into the food, the strength E(x) of the field is given by
𝐸(𝑥) = 𝐸0 𝑒 −𝑥/𝑑
.
Here d is called the penetration depth of the microwaves. This depends not only on the type of the food, but also
how moist it is, and also its salt content. For a typical food, such as a potato (which is mainly starch and water)
the penetration depth is between one and two centimetres. What this means is that the microwaves cannot
penetrate much more than two centimetres into the food. The microwaves themselves heat up the food by
rotating dipoles within it. As these move, they rub against each other, and the food heats up by friction. This
process works very well for water, and hardly at all for ice. Now, the heating power of the microwave food is
proportional to E(x)^2 and this is negligible for depths greater than the penetration depth. Heat is also lost on
the surface of the food (mostly by convection through the air), so the best way to describe microwave cooking is
that the food is cooked from about 1cm underneath its surface. There is an important consequence to this. If
the food is much more than a centimetre or two in depth, then the inside of it may receive very direct heating
from the microwave field at all. The figure below shows the results of a mathematical simulation of the
microwave heating of a (rectangular) container filled with mashed potato. This simulation is achieved by solving
partial differential equations, which describe how the potato is heated. A typical such equation takes the form
below, which expresses the way that heat is generated by the microwave field and redistributed by conduction.
See [7] for more details.
𝑐𝜌𝑇𝑡 = 𝜅𝑇𝑥𝑥 + 𝜀𝜇𝑒 −2𝑥/𝑑
In the figures created from solving this equation, the top shows the results of one minutes heating and the
bottom five minutes. In this figure blue is cold and brown is warm. The conclusion from this simulation is that
even if the outside of the container is hot, the inside can still be quite cool. This is a problem, as it means that
the bacteria in the food may not have been killed by the cooking process.
The conclusion from this simple simulation is that the best way to be sure that your food is warm all over is
either to cook it for a long time, which can burn its contents, or to cook and then wait until the middle warms
up by heat conduction, or to stir the food after you remove it from the microwave cooker. And if you read the
packet of any microwave cookable meal this is exactly what it says that you should do. More sophisticated
mathematical equations can be used to simulate more of the processes involved in microwave cooking and can
thus be used to design better, more efficient and safer cookers. This brings benefits to us all.
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Where are the bees?
It is rumoured that Einstein was once asked what he feared most. And
his response was that he was worried about the bees dying out. There is a
lot of reason to share Einstein’s supposed concerns. It is certainly true
that bees play a hugely important role in pollinating our crops, and
without them many of our plants simply would not grow. Unfortunately,
bees are in crisis. Due to what we believe is a combination of the effects
of parasites (mites in particular), climate change, and the changes of land
use, bee populations in Western Europe are rapidly decreasing. This is a
very real problem, and it is important for our food supply to find out why
the bees are dying out, and how we can prevent this. However, there is a problem in doing this, as it is not easy
to watch bees in their hives without disturbing them. Mathematics comes to the rescue in two different ways.
One is through mathematical modelling. It is possible to formulate equations describing the way that heat is
transferred through a beehive, and in particular to model the way that bees cluster together to keep each other
warm. This helps to explain the way that bees respond to climate change.
A recent development in the application of maths
to the study of bee behaviour comes from the
application of modern techniques of medical
imaging. I will talk about these ideas in a great
deal of detail in a future lecture, but in summary,
one method to look inside a human body, called
computerised axial tomography (CAT) scanning,
is to take a series of X-ray cross sections and to
use mathematical techniques to put all of these
together to give a three dimensional image.
Exactly the same idea can now be used to find out what is going on inside a bee hive, with two important
differences. One is to use a very low dose of X-rays to avoid damaging the bees, and the second is to account
for the inconvenient way that bees tend to move around whilst they are being imaged. These both provide
additional mathematical challenges, but by solving these it is possible to use X-rays to monitor bees directly in
the hive without disturbing them in any way. On the right you can see just such an image of a beehive,
produced by Mark Greco [10]. In this image the red dots are the bees and the yellow the honey. Hopefully
further monitoring in this way will help us preserve the bees for the next genera.
Packing and distributing
Once we have produced food we need to pack it and distribute it.
Both of these processes involve the mathematics of optimisation and
scheduling. Food needs to be transported around in such a way that it
arrives at the correct place, at the correct time, and is fresh when it
arrives. This introduces huge logistical challenges, made worse by the
fact that different foods have different shapes, weights and times of
delivery. We often forget how much planning is needed to (for
example) deliver fresh strawberries to our plate in the middle of winter.
These problems are very hard to solve. A classic example being the
travelling salesman problem, which aims to find the optimal route for a salesman to deliver his goods. Another
example is the knapsack problem which tries to find the best way to fit a set of differently shaped objects into a
knapsack, with direct application to the problem of fitting food into a freezer lorry (illustrated) or a transport
plane. Only relatively recently have efficient (probabilistic based) algorithms been developed to provide an
answer. These algorithms are now making a huge difference to the way that goods are transported all over the
world. To solve this problem uses the mathematical ideas of operational research, and I will return to these in a
future lecture.
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In conclusion: Three mathematicians go into a pub.
I will finish this lecture with a bad story/joke about mathematicians and drinking. You have to
concentrate a bit to get the joke.
Three mathematicians go into a pub and the bar tender asks ‘Does anyone want a lager’.
The first mathematician pauses for thought, and then says, ‘I don't know’.
The second mathematician likewise says, ‘I don't know’.
Finally the third mathematician says, ‘No!’
So the bar tender asks ‘Does everyone want a bitter then?’
The first mathematician pauses for thought, and then again says, ‘I don't know’.
The second mathematician likewise says, ‘I don't know’.
Finally the third mathematician says, ‘Yes!’
So they all have a bitter.
Now, think about this joke as you ponder the rest of the points in this lecture and the very real
contribution that maths makes to feeding the whole of the world.
© Chris Budd, February 2017
References
[1] D. Luenberger, Linear and nonlinear programming, (2008), Springer.
[2] The Wikipedia article on the Simplex Algorithm is given by:
https://en.wikipedia.org/wiki/Simplex_algorithm
[3] See the report on the KTN workshop at Bath on Agri-Science.
http://www.bath.ac.uk/imi/news/Agri-Food.html
[4] D’Arcy Thompson, On Growth and Form, 1917, Cambridge.
[5] https://en.wikipedia.org/wiki/Population_dynamics_of_fisheries
[6] http://news.bbc.co.uk/1/hi/sci/tech/6592693.stm
[7] The work of the MACSI group on Guinness is described in the article
http://ulsites.ul.ie/macsi/sites/default/files/macsi_the_initiation_of_guinness.pdf
[8] C. Budd and A. Hill, A comparison of models and methods for simulating the microwave heating of moist foodstuffs, Int. J.
Heat and Mass Transfer, (2011), 31, pp. 807—817.
[9] S. Howison Practical Applied Mathematics: Modelling, Analysis, Approximation, (2005), Cambridge.
[10] The work of Mark Greco and his team on beehives is described in the article
http://www.bath.ac.uk/research/news/2013/08/22/mark-greco-on-cat-scanning-beehives/
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